AI For Business Development

AI For Business Development — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

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  BioBIKE

  BioBike(nee. BioLingua ) is a cloud-based, through-the-web programmable (Paas) symbolic biocomputing and bioinformatics platform that aims to make computational biology, and especially intelligent biocomputing (that is, the application of Artificial Intelligence to computational biology) accessible to research scientists who are not expert programmers. == Unique capabilities == BioBIKE is an integrated symbolic biocomputing and bioinformatics platform, built from the start as an entirely (what is now called) cloud-based architecture where all computing is done in remote servers, and all user access is accomplished through web browsers. BioBIKE has a built-in frame system in which all objects, data, and knowledge are represented. This enables code written either in the native Lisp, in the visual programming language, or systems of rules expressed in the SNARK theorem prover to access the whole of biological knowledge in an integrated manner. For its time (released in 2002) it was unique in permitting users to create fully functional biocomputing programs that run on the back-end servers entirely through the web browser UI. (In modern terms it was one of the first PaaS (Platform as a Service) systems, predating even Salesforce in this capability.) Initially this programming was carried out in raw Lisp, but Jeff Elhai's team at VCU, with NSF funding, created an entirely graphical programming environment on top of BioBIKE based upon the Boxer-style programming environments. Being a multi-headed, multi-threaded, multi-user, multi-tenancy cloud-based system, BioBIKE users were able to directly work together through their web browsers, remotely sharing the same listener and memory space. This permitted a unique sort of collaboration, discussed in Shrager (2007). A specialized offshoot of BioBIKE called "BioDeducta" includes SRI's SNARK theorem prover, offering unique "deductive biocomputing" capabilities. == Implementation == BioBIKE is open-source software implemented using the Lisp programming language. Continuing development takes place by the BioBIKE team centered at Virginia Commonwealth University . == History == BioBIKE was originally called "BioLingua", and was developed by Jeff Shrager at The Carnegie Inst. of Washington Dept. of Plant Biology, and JP Massar with funding from NASA's Astrobiology Division. Shrager and Massar wanted to create a web-based, multi-user Lisp Machine, specialized for bioinformatics. Other early contributors to the project included Mike Travers, and Jeff Elhai of VCU. Elhai obtained continuing funding from the National Science Foundation for the project, which was renamed BioBIKE. Elhai and colleagues added BioBIKE's unique visual programming language. Shrager, meanwhile, collaborated with Richard Waldinger at SRI to build SRI's (SNARK) theorem prover into BioBIKE, creating a deductive biocomputing system, called BioDeducta. == Instances == There used to be a number of BioBIKE verticals in different biological domains, including viral pathogens, cyanobacteria and other bacteria, Arabidopsis thaliana, and several others described in the references.

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  Vulnerabilities Equities Process

  The Vulnerabilities Equities Process (VEP) is a process used by the U.S. federal government to determine on a case-by-case basis how it should treat zero-day computer security vulnerabilities: whether to disclose them to the public to help improve general computer security, or to keep them secret for offensive use against the government's adversaries. The VEP was first developed during the period 2008–2009, but only became public in 2016, when the government released a redacted version of the VEP in response to a FOIA request by the Electronic Frontier Foundation. Following public pressure for greater transparency in the wake of the Shadow Brokers affair, the U.S. government made a more public disclosure of the VEP process in November 2017. == Participants == According to the VEP plan published in 2017, the Equities Review Board (ERB) is the primary forum for interagency deliberation and determinations concerning the VEP. The ERB meets monthly, but may also be convened sooner if an immediate need arises. The ERB consists of representatives from the following agencies: Office of Management and Budget Office of the Director of National Intelligence (including the Intelligence Community-Security Coordination Center) United States Department of the Treasury United States Department of State United States Department of Justice (including the Federal Bureau of Investigation and the National Cyber Investigative Joint Task Force) Department of Homeland Security (including the National Cybersecurity and Communications Integration Center and the United States Secret Service) United States Department of Energy United States Department of Defense (to include the National Security Agency, including Information Assurance and Signals Intelligence elements), United States Cyber Command, and DoD Cyber Crime Center) United States Department of Commerce Central Intelligence Agency The National Security Agency serves as the executive secretariat for the VEP. == Process == According to the November 2017 version of the VEP, the process is as follows: === Submission and notification === When an agency finds a vulnerability, it will notify the VEP secretariat as soon as is possible. The notification will include a description of the vulnerability and the vulnerable products or systems, together with the agency's recommendation to either disseminate or restrict the vulnerability information. The secretariat will then notify all participants of the submission within one business day, requesting them to respond if they have an relevant interest. === Equity and discussions === An agency expressing an interest must indicate whether it concurs with the original recommendation to disseminate or restrict within five business days. If it does not, it will hold discussions with the submitting agency and the VEP secretariat within seven business days to attempt to reach consensus. If no consensus is reached, the participants will suggest options for the Equities Review Board. === Determination to disseminate or restrict === Decisions whether to disclose or restrict a vulnerability should be made quickly, in full consultation with all concerned agencies, and in the overall best interest of the competing interests of the missions of the U.S. government. As far as possible, determinations should be based on rational, objective methodologies, taking into account factors such as prevalence, reliance, and severity. If the review board members cannot reach consensus, they will vote on a preliminary determination. If an agency with an equity disputes that decision, they may, by providing notice to the VEP secretariat, elect to contest the preliminary determination. If no agency contests a preliminary determination, it will be treated as a final decision. === Handling and follow-on actions === If vulnerability information is released, this will be done as quickly as possible, preferably within seven business days. Disclosure of vulnerabilities will be conducted according to guidelines agreed on by all members. The submitting agency is presumed to be most knowledgeable about the vulnerability and, as such, will be responsible for disseminating vulnerability information to the vendor. The submitting agency may elect to delegate dissemination responsibility to another agency on its behalf. The releasing agency will promptly provide a copy of the disclosed information to the VEP secretariat for record keeping. Additionally, the releasing agency is expected to follow up so the ERB can determine whether the vendor's action meets government requirements. If the vendor chooses not to address a vulnerability, or is not acting with urgency consistent with the risk of the vulnerability, the releasing agency will notify the secretariat, and the government may take other mitigation steps. == Criticism == The VEP process has been criticized for a number of deficiencies, including restriction by non-disclosure agreements, lack of risk ratings, special treatment for the NSA, and less than whole-hearted commitment to disclosure as the default option. == UK equivalent == British intelligence agencies—GCHQ in particular—follow a similar approach, also known as the Equities Process, to determine whether to disclose or retain security vulnerabilities. The Investigatory Powers Act 2016 was amended in 2022 to bring oversight of the operation of the process within the remit of the Investigatory Powers Commissioner. Details of the process were made public in 2018.

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  Human Race Machine

  The Human Race Machine (HRM) is a computerized console composed of four different programs. The Human Race Machine program allows participants to see themselves with the facial characteristics of six different races: Asian, White, African, Middle Eastern, and Indian, mapped onto their own face. The Age Machine allows viewers see an aged version of his or her face. A version of this methodology has been used for over twenty years by the FBI and the National Center for Missing and Exploited Children to help locate kidnap victims and missing children. The Couples Machine combines photographs of two people in different percentages to show the appearance of their child. The Anomaly Machine lets viewers see themselves with facial anomalies. The HRM was created by artist Nancy Burson and David Kramlich; it uses morphing technology. It was shown on Oprah on 2006-02-16.

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  Radioplayer

  Radioplayer is a radio technology platform, owned by UK radio broadcasters and operated under licence in some other countries. It operates an internet radio web tuner, a set of mobile phone apps, an in-car adaptor, and a growing range of integrations with other connected devices and platforms. Radioplayer is operated by UK Radioplayer Ltd which is a not-for-profit organisation owned by UK radio broadcasters. Initial shareholders were the BBC, Global Radio, GMG Radio, Absolute Radio and RadioCentre. After consolidation in the radio market, current shareholders are the BBC, Global Radio, Bauer Media Group and RadioCentre. == History == Launched in the UK on 31 March 2011, Radioplayer set out to offer a simple and accessible way to listen to radio via the internet. It contained 157 stations at launch. Initially working internally at the BBC for Tim Davie, then Director of BBC Audio & Music, Michael Hill led the project since March 2009; he was made Managing Director of UK Radioplayer Ltd on 28 July 2010. At launch, Radioplayer was a simple and straightforward Flash-based radio player, linked-to by radio stations on their own website. The player included searching and bookmarking across all of UK radio station content. On 5 October 2012, Radioplayer launched a mobile app on iOS phones with an Android version following shortly afterwards. The apps are unavailable for download outside the United Kingdom. This was followed by a tablet app on 25 September 2013. The apps also support Android Wear, Android Auto, Smart Device Link, Apple Watch and Apple CarPlay. They are also compatible with Chromecast and Airplay. In September 2016, Radioplayer announced it had been chosen by Amazon to integrate with their new voice-controlled 'Echo' device, ahead of its UK launch. In July 2017, Radioplayer integrated with the Sonos and Bose multi-room speaker platforms. UK Radioplayer currently contains around 500 UK stations, from Ofcom-licensed broadcasters. Online-only 'sister-stations' can also be added, but only by broadcasters with Ofcom licences which have been on the platform for over a year. == Radioplayer Car == Radioplayer Car was announced in September 2014 as a hybrid radio receiver that switches between FM, DAB and streaming to find the strongest signal. Speaking in Oslo in June 2015, Michael Hill said that he hoped to launch the product in the UK and Norway during the summer of 2015. In February 2017, Radioplayer Car was launched. It was marketed as the world’s first voice-controlled hybrid radio adaptor for car stereos. A small box, fitted behind the dashboard, links to the auxiliary input on an existing car radio. It connects wirelessly via Bluetooth to the driver’s smartphone by an app. The adaptor enabled drivers to listen to their own smartphone music collections using Bluetooth, take hands-free calls, listen to inbound text messages and receive instant audio travel news, customised by GPS to their location and direction of travel. The hardware was manufactured under licence by car audio interfaces supplier Connects2, and Hyde Park Corner was promoted as the preferred installer of the audio equipment. There were several spin-off benefits of the Radioplayer Car project, including the creation of the hybrid radio metadata API for cars, known as the 'WRAPI' (Worldwide Radioplayer API). == International == Through a separate company called Radioplayer Worldwide, Radioplayer technology is licensed to a number of different territories.

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  Sprayprinter

  SprayPrinter is a device that attaches to aerosol paint cans whereby users can print images via Bluetooth from a smartphone onto a wall or almost any surface. == History == The technology behind SprayPrinter was developed by Mihkel Joala. He explained in a 2016 interview with New Atlas that his idea was inspired by the modern car engine and the Nintendo Wii console. "Engines nowadays use extremely fast valves to spray fuel to [the] combustion chamber," says Joala. "I realized I can use them to shoot paint with pinpoint accuracy." As of December 2021, the company appears to be no longer selling products. == Awards and Recognitions == In 2015, SprayPrinter received €8,000 from the Estonian prototyping contest Prototron for its initial prototype. In 2016, the SprayPrinter team won the grand prize of €30,000 from the televised pitching competition Ajujaht.

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  Mixed raster content

  Mixed raster content (MRC) is a method for compressing images that contain both binary-compressible text and continuous-tone components, using image segmentation methods to improve the level of compression and the quality of the rendered image. By separating the image into components with different compressibility characteristics, the most efficient and accurate compression algorithm for each component can be applied. MRC-compressed images are typically packaged into a hybrid file format such as DjVu and sometimes PDF. This allows for multiple images, and the instructions to properly render and reassemble them, to be stored within a single file. Some image scanners optionally support MRC when scanning to PDF. A typical manual states that without MRC, the image is generated in a single process, with text and graphics not distinguished. With MRC, separate processes are used for text, graphics, and other elements, producing clearer graphics and sharper text, at the price of slightly slower processing. MRC is recommended to optimise the scanning of documents with harder-to-read text or lower-quality graphics. MRC can also reduce the size of the scanned file, though higher compression using JBIG2 can sometimes lead to character substitution errors in scanned documents. == File format == A form of MRC is defined by international standard bodies as ISO/IEC 16485, or ITU recommendation T.44 (accessible free of charge). It defines a file format with bilevel masks and two data layers in each "stripe" of the image. The mask can be encoded in ITU T.4, JBIG1, or JBIG2, while the images can be JPEG, JBIG1, or run-length encoded color. The format is loosely based on JPEG, with a APP13 segment registered for this purpose. It is not known whether this file format is actually used, as formats like DjVu and PDF have their own ways of defining layers and masks.

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  Image texture

  An image texture is the small-scale structure perceived on an image, based on the spatial arrangement of color or intensities. It can be quantified by a set of metrics calculated in image processing. Image texture metrics give us information about the whole image or selected regions. Image textures can be artificially created or found in natural scenes captured in an image. Image textures are one way that can be used to help in segmentation or classification of images. For more accurate segmentation the most useful features are spatial frequency and an average grey level. To analyze an image texture in computer graphics, there are two ways to approach the issue: structured approach and statistical approach. == Structured approach == A structured approach sees an image texture as a set of primitive texels in some regular or repeated pattern. This works well when analyzing artificial textures. To obtain a structured description a characterization of the spatial relationship of the texels is gathered by using Voronoi tessellation of the texels. == Statistical approach == A statistical approach sees an image texture as a quantitative measure of the arrangement of intensities in a region. In general this approach is easier to compute and is more widely used, since natural textures are made of patterns of irregular subelements. === Edge detection === The use of edge detection is to determine the number of edge pixels in a specified region, helps determine a characteristic of texture complexity. After edges have been found the direction of the edges can also be applied as a characteristic of texture and can be useful in determining patterns in the texture. These directions can be represented as an average or in a histogram. Consider a region with N pixels. the gradient-based edge detector is applied to this region by producing two outputs for each pixel p: the gradient magnitude Mag(p) and the gradient direction Dir(p). The edgeness per unit area can be defined by F e d g e n e s s = | { p | M a g ( p ) > T } | N {\displaystyle F_{edgeness}={\frac {|\{p|Mag(p)>T\}|}{N}}} for some threshold T. To include orientation with edgeness histograms for both gradient magnitude and gradient direction can be used. Hmag(R) denotes the normalized histogram of gradient magnitudes of region R, and Hdir(R) denotes the normalized histogram of gradient orientations of region R. Both are normalized according to the size NR Then F m a g , d i r = ( H m a g ( R ) , H d i r ( R ) ) {\displaystyle F_{mag,dir}=(H_{mag}(R),H_{dir}(R))} is a quantitative texture description of region R. === Co-occurrence matrices === The co-occurrence matrix captures numerical features of a texture using spatial relations of similar gray tones. Numerical features computed from the co-occurrence matrix can be used to represent, compare, and classify textures. The following are a subset of standard features derivable from a normalized co-occurrence matrix: A n g u l a r 2 n d M o m e n t = ∑ i ∑ j p [ i , j ] 2 C o n t r a s t = ∑ i = 1 N g ∑ j = 1 N g n 2 p [ i , j ] , where | i − j | = n C o r r e l a t i o n = ∑ i = 1 N g ∑ j = 1 N g ( i j ) p [ i , j ] − μ x μ y σ x σ y E n t r o p y = − ∑ i ∑ j p [ i , j ] l n ( p [ i , j ] ) {\displaystyle {\begin{aligned}Angular{\text{ }}2nd{\text{ }}Moment&=\sum _{i}\sum _{j}p[i,j]^{2}\\Contrast&=\sum _{i=1}^{Ng}\sum _{j=1}^{Ng}n^{2}p[i,j]{\text{, where }}|i-j|=n\\Correlation&={\frac {\sum _{i=1}^{Ng}\sum _{j=1}^{Ng}(ij)p[i,j]-\mu _{x}\mu _{y}}{\sigma _{x}\sigma _{y}}}\\Entropy&=-\sum _{i}\sum _{j}p[i,j]ln(p[i,j])\\\end{aligned}}} where p [ i , j ] {\displaystyle p[i,j]} is the [ i , j ] {\displaystyle [i,j]} th entry in a gray-tone spatial dependence matrix, and Ng is the number of distinct gray-levels in the quantized image. One negative aspect of the co-occurrence matrix is that the extracted features do not necessarily correspond to visual perception. It is used in dentistry for the objective evaluation of lesions [DOI: 10.1155/2020/8831161], treatment efficacy [DOI: 10.3390/ma13163614; DOI: 10.11607/jomi.5686; DOI: 10.3390/ma13173854; DOI: 10.3390/ma13132935] and bone reconstruction during healing [DOI: 10.5114/aoms.2013.33557; DOI: 10.1259/dmfr/22185098; EID: 2-s2.0-81455161223; DOI: 10.3390/ma13163649]. === Laws texture energy measures === Another approach is to use local masks to detect various types of texture features. Laws originally used four vectors representing texture features to create sixteen 2D masks from the outer products of the pairs of vectors. The four vectors and relevant features were as follows: L5 = [ +1 +4 6 +4 +1 ] (Level) E5 = [ -1 -2 0 +2 +1 ] (Edge) S5 = [ -1 0 2 0 -1 ] (Spot) R5 = [ +1 -4 6 -4 +1 ] (Ripple) To these 4, a fifth is sometimes added: W5 = [ -1 +2 0 -2 +1 ] (Wave) From Laws' 4 vectors, 16 5x5 "energy maps" are then filtered down to 9 in order to remove certain symmetric pairs. For instance, L5E5 measures vertical edge content and E5L5 measures horizontal edge content. The average of these two measures is the "edginess" of the content. The resulting 9 maps used by Laws are as follows: L5E5/E5L5 L5R5/R5L5 E5S5/S5E5 S5S5 R5R5 L5S5/S5L5 E5E5 E5R5/R5E5 S5R5/R5S5 Running each of these nine maps over an image to create a new image of the value of the origin ([2,2]) results in 9 "energy maps," or conceptually an image with each pixel associated with a vector of 9 texture attributes. === Autocorrelation and power spectrum === The autocorrelation function of an image can be used to detect repetitive patterns of textures. == Texture segmentation == The use of image texture can be used as a description for regions into segments. There are two main types of segmentation based on image texture, region based and boundary based. Though image texture is not a perfect measure for segmentation it is used along with other measures, such as color, that helps solve segmenting in image. === Region based === Attempts to group or cluster pixels based on texture properties. === Boundary based === Attempts to group or cluster pixels based on edges between pixels that come from different texture properties.

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  Film-out

  Film-out is the process in the computer graphics, video production and filmmaking disciplines of transferring images or animation from videotape or digital files to a traditional film print. Film-out is a broad term that encompasses the conversion of frame rates, color correction, as well as the actual printing, also called scannior recording. The film-out process is different depending on the regional standard of the master videotape in question – NTSC, PAL, or SECAM – or likewise on the several emerging region-independent formats of high definition video (HD video); thus each type is covered separately, taking into account regional film-out industries, methods and technical considerations. == Live action video == Many modern documentaries and low-budget films are shot on videotape or other digital video media, instead of film stock, and completed as digital video. Video production means substantially lower costs than 16 mm or 35 mm film production on all levels. Until recently, the relatively low cost of video ended when the issue of a theatrical presentation was raised, which required a print for film projection. With the growing presence of digital projection, this is becoming less of a factor. === Standard definition (SD) video === Film-out of standard-definition video – or any source that has an incompatible frame rate – is the up-conversion of video media to film for theatrical viewing. The video-to-film conversion process consists of two major steps: first, the conversion of video into digital film frames which are then stored on a computer or on HD videotape; and secondly, the printing of these digital film frames onto actual film. To understand these two steps, it is important to understand how video and film differ. Film (sound film, at least) has remained unchanged for almost a century and creates the illusion of moving images through the rapid projection of still images, frames, upon a screen, typically 24 per second. Traditional interlaced SD video has no real frame rate, (though the term frame is applied to video, it has a different meaning). Instead, video consists of a very fast succession of horizontal lines that continually cascade down the television screen – streaming top to bottom, before jumping back to the top and then streaming down to the bottom again, repeatedly, almost 60 alternating screen-fulls every second for NTSC, or exactly 50 such screen-fulls per second for PAL and SECAM. Since visual movement in video is infused in this continuous cascade of scan lines, there is no discrete image or real frame that can be identified at any one time. Therefore, when transferring video to film, it is necessary to invent individual film frames, 24 for every second of elapsed time. The bulk of the work done by a film-out company is this first step, creating film frames out of the stream of interlaced video. Each company employs its own (often proprietary) technology for turning interlaced video into high-resolution digital video files of 24 discrete images every second, called 24 progressive video or 24p. The technology must filter out all the visually unappealing artifacting that results from the inherent mismatch between video and film movement. Moreover, the conversion process usually requires human intervention at every edit point of a video program, so that each type of scene can be calibrated for maximum visual quality. The use of archival footage in video especially calls for extra attention. Step two, the scanning to film, is the rote part of the process. This is the mechanical step where lasers print each of the newly created frames of the 24p video, stored on computer files or HD videotape, onto rolls of film. Most companies that do film-out, do all the stages of the process themselves for a lump sum. The job includes converting interlaced video into 24p and often a color correction session – (calibrating the image for theatrical projection), before scanning to physical film, (possibly followed by color correction of the film print made from the digital intermediary) – is offered. At the very least, film-out can be understood as the process of converting interlaced video to 24p and then scanning it to film. ==== NTSC video ==== NTSC is the most challenging of the formats when it comes to standards conversion and, specifically, converting to film prints. NTSC runs at the approximate rate of 29.97 video frames (consisting of two interlaced screen-fulls of scan lines, called fields, per frame) per second. In this way, NTSC resolves actual live action movement at almost – but not quite – 60 alternating half-resolution images every second. Because of this 29.97 rate, no direct correlation to film frames at 24 frames per second can be achieved. NTSC is hardest to reconcile with film, thus motivating its own unique processes. ==== PAL and SECAM video ==== PAL and SECAM run at 25 interlaced video frames per second, which can be slowed down or frame-dropped, then deinterlaced, to correlate frame for frame with film running at 24 actual frames per second. PAL and SECAM are less complex and demanding than NTSC for film-out. PAL and SECAM conversions do agitate, though, with the unpleasant choice between slowing down video (and audio pitch, noticeably) by four percent, from 25 to 24 frames per second, in order to maintain a 1:1 frame match, slightly changing the rhythm and feel of the program; or maintaining original speed by periodically dropping frames, thereby creating jerkiness and possible loss of vital detail in fast-moving action or precise edits. === High definition (HD) digital video === High definition digital video can be shot at a variety of frame rates, including 29.97 interlaced (like NTSC) or progressive; or 25 interlaced (like PAL) or progressive; or even 24-progressive (just like film). HD, if shot in 24-progressive, scans nearly perfectly to film without the need for a frame or field conversion process. Other issues remain though, based on the different resolutions, color spaces, and compression schemes that exist in the high-definition video world. == Computer graphics and animation == Artists working with CGI-Computer-generated imagery animation computers create pictures frame by frame. Once the finished product is done, the frames are outputted, normally in a DPX file. These picture data files can then be put on to film using a film recorder for film out. SGI computers started the high-end CGI-Computer-generated imagery animation systems, but with faster computers and the growth of Linux-based systems, many others are on the market now. Movies fully rendered and animated in CGI such as Toy Story, and Antz utilize the film-out method to produce 35mm copies for archival and release prints. Most CGI work is done in 2K Display resolution files (about the size of QXGA) and then output to the Film-out device for creation of 35 mm elements. With 4K Display resolution digital intermediates on the rise, newer types of film-out recorders are being developed to accept 4k resolution files. A 2K movie requires a Storage Area Network storage several terabytes in size to be properly stored and played out. Computer graphics files are handled the same way but in single frames and may use DPX, TIFF or other file formats. == Digital intermediates == Film-out-recording is the last step of digital intermediate workflow. DPX files that were scanned on a motion picture film scanner are stored on a storage area network (often abbreviated as SAN). The scanned DPX footage is edited and composited-FX on workstations, then mastered back on film. Film restoration is also done this way. A "film intermediate" is an analog variation of a digital intermediate, where a project shot on digital video is printed onto film stock and transferred back to digital video to emulate film. The term was coined after it was used on the Oscar-winning 2012 short film "Curfew". The process was also used on the films Dune (2021) and The Batman (2022). == Images for graphic design and print industries == The days of newspapers and magazines shooting 35mm film are almost gone. Digital cameras can now shoot all the images needed, storing them as files (e.g. JPEG, DPX or another format) that are readily edited prior to use. Once the final copy is approved, it can be filmed out for publishing. Digital stills are not the only way to get pictures used in the graphic design and print industries. Film scanners and computer graphics programs are also common sources for graphic design and print industries. == Types of devices == The following devices are used in film-out processes: CRT recorder. Camera and a special TV display Kinescope – early type Electronic Video Recording or EVR – early type EBR Electron Beam Film Recorder 16 mm by 3M Laser film recorder, like Kodak's high-end Lightning II recorder and Arri's Arrilaser. DLP Film recorder, like Cinevation's real-time Cinevator. == History == Lately it has become possible to transfer video images, inclu

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  Cloudflare

  Cloudflare, Inc., is an American technology company headquartered in San Francisco, California, that provides a range of internet services, including content delivery network (CDN) services, cloud cybersecurity, DDoS mitigation, and ICANN-accredited domain registration. The company's services act primarily as a reverse proxy between website visitors and a customer's hosting provider, improving performance and protecting against malicious traffic. Cloudflare was founded in 2009 by Matthew Prince, Lee Holloway, and Michelle Zatlyn. The company went public on the New York Stock Exchange in 2019 under the ticker symbol NET. Cloudflare has since expanded its offerings to include edge computing through its Workers platform, a public DNS resolver (1.1.1.1), and a VPN-like service known as WARP. In recent years, the company has integrated artificial intelligence into its infrastructure, acquiring companies such as Replicate and launching tools to manage AI bots and scrapers. According to W3Techs, Cloudflare is used by approximately 21.3% of all websites on the Internet as of January 2026. The company has been the subject of controversy regarding its policy of content neutrality. While Cloudflare executives have historically advocated for remaining a neutral infrastructure provider, the company has terminated services for specific high-profile websites associated with hate speech and violence, including The Daily Stormer, 8chan, and Kiwi Farms, following significant public pressure. Cloudflare has also faced criticism and litigation regarding copyright infringement by websites using its services, notably losing a lawsuit against Japanese publishers in 2025. The company experienced significant global outages in late 2025 which disrupted services for major platforms internationally. == History == Cloudflare was founded on July 26, 2009, by Matthew Prince, Lee Holloway, and Michelle Zatlyn. Prince and Holloway had previously collaborated on Project Honey Pot, a product of Unspam Technologies that partly inspired the basis of Cloudflare. In 2009, the company was venture-capital funded. On August 15, 2019, Cloudflare submitted its S-1 filing for an initial public offering on the New York Stock Exchange under the stock ticker NET. It opened for public trading on September 13, 2019, at $15 per share. According to the company, the name 'Cloudflare' was chosen, over the initial 'WebWall', because it best described what they were trying to do: build a "firewall in the cloud." In 2020, Cloudflare co-founder and COO Michelle Zatlyn was named president. Cloudflare has acquired web-services and security companies, including StopTheHacker (February 2014), CryptoSeal (June 2014), Eager Platform Co. (December 2016), Neumob (November 2017), S2 Systems (January 2020), Linc (December 2020), Zaraz (December 2021), Vectrix (February 2022), Area 1 Security (February 2022), Nefeli Networks (March 2024), BastionZero (May 2024), and Kivera (October 2024). Replicate (November 2025), and Human Native (January 2026). Since at least 2017, Cloudflare has used a wall of lava lamps at its San Francisco headquarters as a source of randomness for encryption keys, alongside double pendulums at its London offices and a Geiger counter at its Singapore offices. The lava lamp installation implements the Lavarand method, where a camera transforms the unpredictable shapes of the "lava" blobs into a digital image. In Q4 2022, Cloudflare provided paid services to 162,086 customers. In October 2024, Cloudflare won a lawsuit against patent troll Sable Networks. Sable paid Cloudflare $225,000, granted it a royalty-free license to its patent portfolio, and dedicated its patents to the public by abandoning its patent rights. In November 2025, it was announced Cloudflare had agreed to acquire Replicate, a San Francisco–based platform that enables software developers to run, fine-tune, and deploy open-source machine-learning models via an API without managing infrastructure. In January 2026, Cloudflare released an analysis regarding BGP routing leaks observed from the Venezuelan state-owned ISP CANTV (AS8048), which occurred on January 2 coincides with the arrest of Nicolás Maduro. While some security researchers had speculated that the outages were linked to U.S. cyber operations, Cloudflare's data indicated that the anomalies were consistent with a pattern of "insufficient routing export and import policies" by the ISP rather than malicious external interference. In January 2026, Cloudflare acquired Human Native, an AI data marketplace that brokers transactions between developers and content creators, for an undisclosed amount. On January 16, 2026, Cloudflare acquired The Astro Technology Company, the developers behind the open-source web framework Astro. In May 2026, Cloudflare announced the elimination of approximately 1,100 positions, around 20 percent of its workforce, in a restructuring the company attributed to the rapid adoption of artificial intelligence tools. The announcement coincided with the company's first-quarter 2026 earnings, which reported a record $639.8 million in quarterly revenue, a 34 percent year-over-year increase. CEO Matthew Prince stated the cuts were not driven by performance concerns but reflected roles made obsolete by AI, and that Cloudflare expected to employ more people by the end of 2027 than at any point during 2026. == Products == Cloudflare provides network and security products for consumers and businesses, utilizing edge computing, reverse proxies for web traffic, data center interconnects, and a content distribution network to serve content across its network of servers. It supports transport layer protocols TCP, UDP, QUIC, and many application layer protocols such as DNS over HTTPS, SMTP, and HTTP/2 with support for HTTP/2 Server Push. As of 2023, Cloudflare handles an average of 45 million HTTP requests per second. As of 2024, Cloudflare servers are powered by AMD EPYC 9684X processors. Cloudflare also provides analysis and reports on large-scale outages, including Verizon's October 2024 outage. === Artificial intelligence === In 2023, Cloudflare launched "Workers AI", a framework allowing for use of Nvidia GPU's within Cloudflare's network. In 2024, Cloudflare launched a tool that prevents bots from scraping websites. To build automatic bot detector models, the company analyzed "AI" bots and crawler traffic. The company also launched an "AI" assistant to generate charts based on queries by leveraging "Workers AI". Cloudflare announced plans in September 2024 to launch a marketplace where website owners can sell "AI" model providers access to scrape their site's content. In March 2025, Cloudflare announced a new feature called "AI Labyrinth", which combats unauthorized "AI" data scraping by serving fake "AI"-generated content to LLM bots. In July, the company rolled out a permission-based setting to allow websites to automatically block online bots from scraping data and content. Cloudflare released AutoRAG into beta in 2025. AutoRAG (retrieval augmented generation) creates a vector database of a website's unstructured content to identify relationships between concepts. It is part of an initiative with Microsoft, alongside their NLWeb standard, to make websites easier for people and automated systems to query. Cloudflare and GoDaddy partnered in April 2026 to enable AI Crawl Control features on GoDaddy hosted websites. This would allow site owners to decide how AI bot crawlers interact with their content. === DDoS mitigation === Cloudflare provides free and paid DDoS mitigation services that protect customers from distributed denial of service (DDoS) attacks. Cloudflare received media attention in June 2011 for providing DDoS mitigation for the website of LulzSec, a black hat hacking group. In March 2013, The Spamhaus Project was targeted by a DDoS attack that Cloudflare reported exceeded 300 gigabits per second (Gbit/s). Patrick Gilmore, of Akamai, stated that at the time it was "the largest publicly announced DDoS attack in the history of the Internet". While trying to defend Spamhaus against the DDoS attacks, Cloudflare ended up being attacked as well; Google and other companies eventually came to Spamhaus' defense and helped it to absorb the unprecedented amount of attack traffic. In 2014, Cloudflare began providing free DDoS mitigation for artists, activists, journalists, and human rights groups under the name "Project Galileo". In 2017, it extended the service to electoral infrastructure and political campaigns under the name "Athenian Project". By 2025, more than 2,900 users and organizations were participating in Project Galileo, including 31 US states. In February 2014, Cloudflare claimed to have mitigated an NTP reflection attack against an unnamed European customer, which it stated peaked at 400 Gbit/s. In November 2014, it reported a 500 Gbit/s DDoS attack in Hong Kong. In July 2021, the company claimed to have absorbed a DDoS atta

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  Internet Security Awareness Training

  Internet Security Awareness Training (ISAT) is the training given to members of an organization regarding the protection of various information assets of that organization. ISAT is a subset of general security awareness training (SAT). Even small and medium enterprises are generally recommended to provide such training, but organizations that need to comply with government regulations (e.g., the Gramm–Leach–Bliley Act, the Payment Card Industry Data Security Standard, Health Insurance Portability and Accountability Act, Sarbanes–Oxley Act) normally require formal ISAT for annually for all employees. Often such training is provided in the form of online courses. ISAT, also referred to as Security Education, Training, and Awareness (SETA), organizations train and create awareness of information security management within their environment. It is beneficial to organizations when employees are well trained and feel empowered to take important actions to protect themselves and organizational data. The SETA program target must be based on user roles within organizations and for positions that expose the organizations to increased risk levels, specialized courses must be required. == Coverage == There are general topics to cover for the training, but it is necessary for each organization to have a coverage strategy based on its needs, as this will ensure the training is practical and captures critical topics relevant to the organization. As the threat landscape changes very frequently, organizations should continuously review their training programs to ensure relevance with current trends. Topics covered in ISAT include: Appropriate methods for protecting sensitive information on personal computer systems, including password policy Various computer security concerns, including spam, malware, phishing, social engineering, etc. Consequences of failure to properly protect information, including potential job loss, economic consequences to the firm, damage to individuals whose private records are divulged, and possible civil and criminal law penalties. Being Internet Security Aware means you understand that there are people actively trying to steal data that is stored within your organization's computers. (This often focuses on user names and passwords, so that criminal elements can ultimately get access to bank accounts and other high-value IT assets.) That is why it is important to protect the assets of the organization and stop that from happening. The general scope should include topics such as password security, Email phishing, Social engineering, Mobile device security, Sensitive data security, and Business communications. In contrast, those requiring specialized knowledge are usually required to take technical and in-depth training courses. Suppose an organization determines that it is best to use one of the available training tools on the market, it must ensure it sets objectives that the training can meet, including confirming the training will provide employees with the knowledge to understand risks and the behaviors needed in managing them, actions to take to prevent or detect security incidents, using language easily understandable by the trainees, and ensuring the pricing is reasonable. Organizations are recommended to base ISAT training content on employee roles and their culture; the policy should guide that training for all employees and gave the following as examples of sources of reference materials: National Institute of Standards and Technology (NIST) Special Publication 800-50, Building an Information Technology Security Awareness and Training Program International Standards Organization (ISO) 27002:2013, Information technology—Security techniques—Code of practice for information security controls International Standards Organization (ISO) 27001:2013, Information technology — Security techniques — Information security management systems COBIT 5 Appendix F.2, Detailed Guidance: Services, Infrastructure and Applications Enabler, Security Awareness The training must focus on current threats specific to an organization and the impacts if that materializes as a result of user actions. Including practical examples and ways of dealing with scenarios help users know the appropriate measures to take. It is a good practice to periodically train customers of specific organizations on threats they face from people with malicious intentions. Coverage strategy for SAT should be driven by an organization's policy. It can help truly determine the level of depth of the training and where it should be conducted at a global level or business unit level, or a combination of both. A policy also empowers a responsible party within the organization to run the training. == Importance == Studies show that well-structured security awareness training can significantly reduce the likelihood of cyber incidents caused by human error. According to the Ponemon Institute, organizations that implement regular security training experience up to 70% fewer successful phishing attacks. Additionally, a 2023 Verizon Data Breach Investigations Report found that 74% of breaches involve the human element, highlighting the need for continuous education. Employees are key in whether organizations are breached or not; there must be a policy on creating awareness and training them on emerging threats and actions to take in safeguarding sensitive information and reporting any observed unusual activity within the corporate environment. Research has shown that SAT has helped reduce cyber-attacks within organizations, especially when it comes to phishing, as trainees learned to identify these attack modes and give them the self-assurance to take action appropriately. There is an increase in phishing attacks, and it has become increasingly important for people to understand how to these attacks work, and the actions required to prevent these and SAT has shown a significant impact on the number of successful phishing attacks against organizations. == Compliance Requirements == Various regulations and laws mandate SAT for organizations in specific industries, including the Gramm–Leach–Bliley Act (GLBA) for the financial services, the Federal Information Security Modernization Act of 2014 for federal agencies, and the European Union's General Data Protection Regulation (GDPR). === Federal Information Security Modernization Act === Employees and contractors in federal agencies are required to receive Security Awareness Training annually, and the program needs to address job-related information security risks linked that provide them with the knowledge to lessen security risks. === Health Insurance Portability and Accountability Act === The Health Insurance Portability and Accountability Act has the Security Rule, and Privacy Rule requiring the creation of a security awareness training program and ensuring employees are trained accordingly. === Payment Card Industry Data Security Standard === The Payment Card Industry Security Standards Council, the governing council for stakeholders in the payment industry, formed by American Express, Discover, JCB International, MasterCard, and Visa that developed the DSS as a requirement for the payment industry. Requirement 12.6 requires member organizations to institute a formal security awareness program. There is a published guide for organizations to adhere to when setting up the program. === US States Training Regulations === Some States mandate Security Awareness Training whiles other do not but simply recommend voluntary training. Among states that require the training for its employees include: Colorado (The Colorado Information Security Act, Colorado Revised Statutes 24-37.5-401 et seq.) Connecticut (13 FAM 301.1-1 Cyber Security Awareness Training (PS800)) Florida (Florida Statutes Chapter 282) Georgia (Executive Order GA E.O.182 mandated training within 90 days of issue) Illinois (Cook County) Indiana (IN H 1240) Louisiana (Louisiana Division of Administration, Office of Technology Services p. 52: LA H 633) Maryland (20-07 IT Security Policy) Montana (Mandatory cyber training for executive branch state employees) Nebraska Nevada (agency-by-agency state employee requirement - State Security Standard 123 – IT Security) New Hampshire New Jersey ( NJ A 1654) North Carolina Ohio (IT-15 - Security Awareness and Training) Pennsylvania Texas Utah Vermont Virginia West Virginia (WV Code Section 5A-6-4a) == Training Techniques == Below are some common training techniques, even though some can be blended depending on the operating environment: Interactive video training – This technique allows users to be trained using two-way interactive audio and video instruction. Web-based training – This method allows employees or users to take the training independently and usually has a testing component to determine if learning has taken place. If not, users can be allowed to retake the course and test to ensure there is a complete understanding

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  QANDA

  QANDA (stands for 'Q and A') is an AI-based learning platform developed by Mathpresso Inc., a South Korea-based education technology company. Its best known feature is a solution search, which uses optical character recognition technology to scan problems and provide step-by-step solutions and learning content. As of March 2024, QANDA solved over 6.3 billion questions. QANDA has 90 million total registered users and has reached 8 million monthly active users (MAU) in 50 countries. 90% of the cumulative users are from overseas such as Vietnam and Indonesia. In January 2024, its MathGPT, a math-specific small large language model set a new world record, surpassed Microsoft's 'ToRA 13B', the previous record holder in benchmarks assessing mathematical performance such as 'MATH' (high school math) and 'GSM8K' (grade school math). 'MathGPT' was co-developed with Upstage and KT. In March 2024, Mathpresso launched 'Cramify' (formerly known as Prep.Pie), an AI-powered study material generator designed to create personalized exam prep materials for U.S. college students. It uses generative AI to create customized study materials uploaded by students. Its features include a range of tools including study summarizer and question solver. == History == Co-founder Jongheun ‘Ray’ Lee first came up with the idea of QANDA during his freshman year in college. While he was tutoring to earn money, Lee realized that the quality of education a student receives is greatly based on their location. Lee saw his K-12 students were regularly asking similar questions and realized that these questions were from a pre-selected number of textbooks currently being used in schools. He decided to team up with his high school friend, Yongjae ‘Jake’ Lee to build a platform whereby, one uses a mobile app to scan and submit questions, and students can ask and receive detailed responses. Lee's school friends, Wonguk Jung and Hojae Jeong, joined the team. In June 2015, Mathpresso, Inc. was founded in Seoul, South Korea. In January 2016, Mathpresso's first product QANDA was launched. It supported a Q&A feature between students and tutors. In October 2017, QANDA introduced an AI-based search capability that permitted users to search for answers in seconds. In April 2020, Jake Yongjae Lee(CEO & co-founder) and Ray Jongheun Lee (co-founder) were selected as Forbes 30 under 30 Asia. In June 2021, QANDA raised $50 million in series C funding. Jake Yongjae Lee was recognized as an Innovator Under 35 by MIT Technology Review. In November 2021, QANDA secured a strategic investment from Google. Since its inception, it has received backing in Series C funding from investors namely Google, Yellowdog, GGV Capital, Goodwater Capital, KDB, and SKS Private Equity with participation from SoftBank Ventures Asia, Legend Capital, Mirae Asset Venture Investment, and Smilegate Investment. In September 2023, Mathpresso has raised $8 million (10 billion KRW) from Korea's telecom giant, KT. The total cumulative investment is about 130 million US dollars. The partnership aims to accelerate the development of an education-specific Large Language Model. The company intends to incorporate the LLM model to fortify its AI tutor, which later will be integrated into the existing services: QANDA App, B2B & B2G Saas, and 1:1 online tutoring (QANDA Tutor). == Features == QANDA features OCR-based solution search, one-on-one Q&A tutoring, a study timer. In 2021, QANDA launched additional features, including the premium subscription model that offers unlimited “byte-sized” micro-video lectures and the community feature that enhances collaborative learning. In 2021, QANDA launched QANDA Tutor, a tablet-based 1:1 tutoring service and QANDA Study, a 1:N online school in Vietnam. In 2022, QANDA launched an exam prep feature that offers past exam materials from school via online. This feature is currently available in South Korea. In August 2023, QANDA launched a beta version of an LLM-powered AI Tutor. == Awards and recognition == Best Hidden Gems of 2017 by Google Playstore 2018 AWS AI Startup Challenge Award National representative for the Google AI for Social Good APAC, 2018 Best Self-Improvement Apps of 2018 by Google Playstore GSV Edtech 150 — the Most Transformational Growth Companies in Digital Learning Speaker at the Google App Summit, 2021 Selected as a prospect unicorn company by Korea Technology Finance Corporation in 2023 Winner of G20-DIA Global Pitching in 2023 2021, 2022, 2023 East Asia EdTech 150 by HolonIQ

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  Load file

  A load file in the litigation community is commonly referred to as the file used to import data (coded, captured or extracted data from ESI processing) into a database; or the file used to link images. These load files carry commands, commanding the software to carry out certain functions with the data found in them. Load files are usually ASCII text files that have delimited fields of information. Such load files may have data about documents to be imported into a document management software such as Concordance or Summation. Or they may have the path or directory where images may reside so that the software can link such images to their corresponding records. Some database programs take one load file for importing images and another for importing data while others take only one load file for both pieces of information. OCR or Search-able Text which is considered "data" is also imported into most database programs via the same load files. Though some people prefer to load the OCR into their databases by running a separate command to search and find the desired text. Commonly used databases and their corresponding file extensions are: Summation (DII , CSV), Concordance (OPT, DAT), Sanction (SDT), IPRO (LFP), Ringtail (MDB) and DB/TextWorks (TXT).

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  Content Security Policy

  Content Security Policy (CSP) is a computer security standard introduced to prevent cross-site scripting (XSS), clickjacking and other code injection attacks resulting from execution of malicious content in the trusted web page context. It is a Candidate Recommendation of the W3C working group on Web Application Security, widely supported by modern web browsers. CSP provides a standard method for website owners to declare approved origins of content that browsers should be allowed to load on that website—covered types are JavaScript, CSS, HTML frames, web workers, fonts, images, embeddable objects such as Java applets, ActiveX, audio and video files, and other HTML5 features. == Status == The standard, originally named Content Restrictions, was proposed by Robert Hansen in 2004, first implemented in Firefox 4 and quickly picked up by other browsers. Version 1 of the standard was published in 2012 as W3C candidate recommendation and quickly with further versions (Level 2) published in 2014. As of 2023, the draft of Level 3 is being developed with the new features being quickly adopted by the web browsers. The following header names are in use as part of experimental CSP implementations: Content-Security-Policy – standard header name proposed by the W3C document. Google Chrome supports this as of version 25. Firefox supports this as of version 23, released on 6 August 2013. WebKit supports this as of version 528 (nightly build). Chromium-based Microsoft Edge support is similar to Chrome's. X-WebKit-CSP – deprecated, experimental header introduced into Google Chrome, Safari and other WebKit-based web browsers in 2011. X-Content-Security-Policy – deprecated, experimental header introduced in Gecko 2 based browsers (Firefox 4 to Firefox 22, Thunderbird 3.3, SeaMonkey 2.1). A website can declare multiple CSP headers, also mixing enforcement and report-only ones. Each header will be processed separately by the browser. CSP can also be delivered within the HTML code using a meta tag, although in this case its effectiveness will be limited. Internet Explorer 10 and Internet Explorer 11 also support CSP, but only sandbox directive, using the experimental X-Content-Security-Policy header. A number of web application frameworks support CSP, for example AngularJS (natively) and Django (middleware). Instructions for Ruby on Rails have been posted by GitHub. Web framework support is however only required if the CSP contents somehow depend on the web application's state—such as usage of the nonce origin. Otherwise, the CSP is rather static and can be delivered from web application tiers above the application, for example on load balancer or web server. === Bypasses === In December 2015 and December 2016, a few methods of bypassing 'nonce' allowlisting origins were published. In January 2016, another method was published, which leverages server-wide CSP allowlisting to exploit old and vulnerable versions of JavaScript libraries hosted at the same server (frequent case with CDN servers). In May 2017 one more method was published to bypass CSP using web application frameworks code. == Mode of operation == If the Content-Security-Policy header is present in the server response, a compliant client enforces the declarative allowlist policy. One example goal of a policy is a stricter execution mode for JavaScript in order to prevent certain cross-site scripting attacks. In practice this means that a number of features are disabled by default: Inline JavaScript code